Applications of Model-Based Meta-Analysis in Drug Development

Abstract

Model-based meta-analysis (MBMA) is a quantitative approach that leverages published summary data along with internal data and can be applied to inform key drug development decisions, including the benefit-risk assessment of a treatment under investigation. These risk–benefit assessments may involve determining an optimal dose compared against historic external comparators of a particular disease indication. MBMA can provide a flexible framework for interpreting aggregated data from historic reference studies and therefore should be a standard tool for the model-informed drug development (MIDD) framework.In addition to pairwise and network meta-analyses, MBMA provides further contributions in the quantitative approaches with its ability to incorporate longitudinal data and the pharmacologic concept of dose–response relationship, as well as to combine individual- and summary-level data and routinely incorporate covariates in the analysis.A common application of MBMA is the selection of optimal dose and dosing regimen of the internal investigational molecule to evaluate external benchmarking and to support comparator selection. Two case studies provided examples in applications of MBMA in biologics (durvalumab + tremelimumab for safety) and small molecule (fenebrutinib for efficacy) to support drug development decision-making in two different but well-studied disease areas, i.e., oncology and rheumatoid arthritis, respectively.Important to the future directions of MBMA include additional recognition and engagement from drug development stakeholders for the MBMA approach, stronger collaboration between pharmacometrics and statistics, expanded data access, and the use of machine learning for database building. Timely, cost-effective, and successful application of MBMA should be part of providing an integrated view of MIDD.

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design

“…In addition to extracting the direct information, this also allows to identify similar studies that could be included in further studies or meta-analyses. In the pharmaceutical industry, model based meta-analyses can be used during the drug development process, which helps to leverage (prior) knowledge in order to make informed decisions about the potential of a compound [91] , [92] . Such data could for example contain information from previous clinical studies or information about possible competing products, which can help for example to make informed decisions about optimal dosing or to perform a risk assessment of the compound's profitability [92] .…”

“…KGs (Graph data models) have shown the potential to be a successful framework for the integration of diverse data sets [93] . Effective integration and analysis of the comprehensive data sources could significantly increase the success rate in drug design and chemical safety assessment [92] , [94] , [95] , [96] . Zhang et al [14] made use of the integration of drug - side effect, drug - indication and drug - target information to predict drug - adverse outcome relationships in a KG framework.…”

How can natural language processing help model informed drug development?: a review

“…MIDD involves leveraging quantitative models to inform decision-making in drug development. 5 In the field of MIDD, NLP can be leveraged to extract information out of structured (eg, electronic health records [EHRs]) and unstructured (eg, research documents) data to optimize and/or accelerate various processes in the drug development lifecycle, eg, determining drug–target interaction 6 and drug–drug interaction, 7 biomarker discovery, 8 drug repurposing, 9 , 10 patient-trial matching, 11 model-based meta-analysis, 12 disease progression modeling, 13 and others. 14 NLP platforms perform the role of assessing potential associations between chemical/drug entities, their target proteins, and novel disease-related pathways by extensive analysis of scientific literature.…”

Longitudinal Meta‐Analysis of Historical Parkinson's Disease Trials to Inform Future Trial Design

“…Moreover, trial design elements for treatments intended to alter the disease trajectory, such as sample size, can be established more reliably when aided by a model-based meta-analysis (MBMA), as these elements are highly dependent on reliable estimation of disease progression, as well as its uncertainty, and are provided by the totality of information from multiple clinical trials. 8 The MBMA is a quantitative approach that uses data from multiple trials to inform key drug development decisions by predicting outcomes. 9 Longitudinal MBMA, in particular, makes use of the totality of information to maximize our understanding of the rate of disease progression, the dynamics of both placebo and active treatment response, and the associated between-trial variability.…”

“…For instance, the duration of a trial that would allow for a satisfactory resolution of treatment effect can be determined. Moreover, trial design elements for treatments intended to alter the disease trajectory, such as sample size, can be established more reliably when aided by a model‐based meta‐analysis (MBMA), as these elements are highly dependent on reliable estimation of disease progression, as well as its uncertainty, and are provided by the totality of information from multiple clinical trials 8 …”